AI is reshaping exploratory data analysis by automating foundational tasks like data cleansing, integration, and pattern recognition through machine learning and AI tools.
This shift enables business users and analysts to focus more on interpreting results and generating meaningful insights rather than manual prep work.
AI-powered tools can also quickly uncover hidden relationships in complex data models and produce dynamic visualization dashboards that make exploration more intuitive.
However, while AI helps automate discovery, domain expertise and human judgment remain essential to ensure analytics insights are contextually relevant and aligned with scientific and regulatory expectations.
When trained on the complexities of clinical trial data and operations, AI models can evolve into trusted oversight tools, not just for analysts, but for entire study teams.
“This supports proactive decision-making and collaborative insight generation,” explains Michelle Lane, Vice President of Data Management at LabConnect.
Introduction to AI tools for data analysis
Olga Kupriyanova, Director of AI and Data Engineering at ISG, says AI is hugely beneficial for fixing some of the core challenges that have existed since the onset of big data analytics in the business space.
She says the promise of big data is the ability to learn from it and do a lot with it; however, humans struggle to isolate and “dig into” millions of transactional records and identify trends or insights that matter.
“How do you find some causational explanations for those trends, if they are available?” she asks. “Manually, this is an impossible task.”
AI tools can now take this aspect of data analysis work and make it entirely machine-driven so that no executive ever misses any signal in the data when AI is at the wheel.
“The ability to leverage code generation to help with data manipulation tasks can rapidly improve the time spent ‘munging’ data so it can be analytically useful,” Kupriyanova says.
A great deal of a data analyst’s work is figuring out how best to handle data that is not clean – e.g., it has missing rows, duplicates, special characters, blank values, or other messy formats.
It also helps deal with unstructured data either through labeling or categorization work, which can help provide a framework to dig deeper into large unstructured datasets to identify useful signals and business insights.
Top AI tools for data analysis in 2026
Dr. Ryan Ries, Chief AI and Data Scientist at Mission, says analysts should remain focused on AI tools that seamlessly integrate into enterprise platforms and business ecosystems.
“If you are on AWS, then Glue, Q for Quicksight, and Bedrock are AI tools you should know,” he says.
He adds that AWS platforms are crucial as they balance scalability, security, and governance.
“Beyond this, analysts should have fluency in GenAI tools that help with code generation, data transformation, and machine learning,” Ries explains.
Combined with statistical learning, these AI-powered tools are key in creating a competitive edge by enabling analysts to quickly prototype, validate, and deploy AI-driven analytics models within production environments.
Kupriyanova says that data analytics, AI-assisted reporting, and dashboarding for proactive AI-driven alerts and insights, including MS Fabric and Tableau, are critical for business users.
Right now, the pace of change means that data analysis, like software development, will make significant strides as the barriers to problem-solving come down and the outcome vs. time spent becomes more non-linear.
“This can either mean that more gets done, or that less time is spent doing it,” she says. “But all of that will hinge on being able to evaluate, learn, and use AI tools to do the work.”
Importance of data visualization in analytics
Data visualization and interpretability are essential for translating machine learning outputs into actionable business insights.
Models must be transparent, explainable, and auditable to meet validation and submission standards for generating trustworthy, compliant, and actionable insights that support clinical research success.
Lane explains she uses Microsoft Power BI to manage and visualize data across our organization, and AI is helping take that to the next level.
“AI features in Power BI help us automate a lot of that work,” she explains. “It can detect issues, analyze the data to see what's driving them, then provide actions to take in response.”
Lane says choosing the right chart type is key. For example, line charts to show trends over time and bar charts for comparisons.
“If the dashboard includes AI-generated insights, we make sure there’s a short explanation or confidence level, so users know how the insight was created,” she says.
Dashboards are tested with real users and adjusted based on their feedback, which helps make sure the dashboards are easy to use and helpful.
“We regularly check the data and AI outputs to make sure everything stays accurate as the trial evolves,” Lane says. “The goal is to keep things clear, reliable, and focused on helping people make better decisions.”
AI in data analytics: Measuring ROI
Measuring ROI for AI-driven analytics solutions requires a comprehensive approach that captures both tangible and intangible benefits for business teams and users.
Lane says analysis teams should start by identifying quantitative metrics that reflect operational efficiencies, such as reduced data processing time, lower error rates, and less manual reconciliation through automated workflows.
“These improvements often lead to cost savings and increased throughput,” she says.
Beyond operational gains, Lane says it’s important to assess qualitative impacts like faster, more informed decision-making, improved data transparency, and the ability to generate insights that influence clinical or business outcomes.
Ries explains that properly measuring ROI means aligning AI initiatives with business outcomes, and not simply technical metrics, recommending IT teams begin by identifying high-impact use cases.
From there, they can measure ROI in terms of efficiency gains, accuracy improvements, and revenue growth.
“Ultimately, the bottom line is that ROI is a result of moving AI out of experimentation and into production, where measurable value can be consistently observed,” Ries says.
Elevate your professional skills and future-proof your career—start mastering data analysis and AI with CompTIA’s Data Analysis Essentials, AI Essentials, and AI Prompting Essentials today.